• Proceedings of the Korean Operations and Management Science Society Conference
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• 1995.04a
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• pp.221-231
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• 1995

This paper proposes that a GDSS research boundary needs to be further expanded to include more human-nature variables arising in natural group interaction processes. By doing so, we can better understand the GDSS phenomena and have a better picture for designing effective GDSS for group decisions. Specifically, this paper reviewed literature and explored variables to be further included in GDSS research arena. As results, this paper proposed an extended framework for GDSS research that includes variables that have been little considered in most empirical studies to date. Then, the variables and their relationships are used to explain the handicaps of current GDSS uses and to redirect the ways of utilizing GDSS technologies for group decision-making. Based on the framework, we suggested proposititions for the future research.

• Journal of the Korean Data and Information Science Society
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• v.21 no.4
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• pp.729-736
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• 2010

In this paper, we study the influence analysis of admission variables including their characteristics on academic achievements of freshmen at K university in Busan. First, multiple regression analysis is used to examine the main effects of admission variables including students' characteristics on the academic achievements. Also, Decision tree analysis is used to examine the interaction effects for the admission variables on the academic achievements. The results of this paper may be helpful to K university in designing effective admissions strategies for recruiting students.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.11
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• pp.2804-2811
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• 2000

A Nordsick form opf the multirate integration scheme has been proposed for flexible multibody dynamic systems. It is assumed that vibrational modal coordinates in the equations of motion are treated as fast variables, whereas the relative joint coordinates are treated as slow variables. In the multirate integration, the fast variables are integrated with small step-size, and the slow variables are integrated with larger step-size. The proposed multirate integration method is based on the Adams-Bashforth-Moulton predictor-corrector method and implemented in the Nordsieck vector form. The Nordsieck form of multrate integration method provides effective step-size control and at the same time, inherits the efficiency from the Adams integration method. Simulations of a flexible gun and turret system of the military tank have been carried out to show the effectiveness and efficiency of the proposed method.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.894-899
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• 2000

Optimization of 26 design variables selected from suspension characteristics for Korean High Speed Train (KHST) is performed according to the minimization of 58 responses which represent running safety and ride comfort for KHST and analyzed by using the each response surface model from stochastic design experiments. Sensitivity of design variables is also analyzed through the response surface model which ineffective design prameters to the performance index are screened by using stepwise regression method. The response surface models are used for optimizing design variables through simplex algorism. Values of performance index simulated by optimized design parameters are totally lower than those by initial design parameters. It shows that this method is effective for optimizing multi-design variables to multi-object function.

• Communications for Statistical Applications and Methods
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• v.20 no.4
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• pp.301-309
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• 2013

This paper predicts gross box office revenue for domestic films using the Korean film data from 2008-2011. We use three regression methods, Linear Regression, Random Forest and Gradient Boosting to predict the gross box office revenue. We only consider domestic films with a revenue size of at least KRW 500 million; relevant explanatory variables are chosen by data visualization and variable selection techniques. The key idea of analyzing this data is to construct the meaningful explanatory variables from the data sources available to the public. Some variables must be categorized to conduct more effective analysis and clustering methods are applied to achieve this task. We choose the best model based on performance in the test set and important explanatory variables are discussed.

• Journal of the Korean Home Economics Association
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• v.49 no.8
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• pp.37-46
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• 2011

Using the 1987-2008 quarterly aggregated data of the Household Income and Expenditure Survey, this study investigated the factors influencing household saving rate. The independent variables in the AR regression model were the GDP growth rate, shares of the total household expenditure allocated to tax & social insurance, and education, the variables reflecting the conditions of the asset market including interest rate, stock market index, and real estate price index, and the variables representing the social economic conditions including the index of aging and income inequality. Among the independent variables interest rate, stock market index, and income inequality were found to be significantly associated with the household saving rate. These results suggested that the redistribution and financial market policies favorable to savers may be effective for raising the household saving rate.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.505-516
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• 2015

We consider sparse high-dimensional linear regression models. Penalized regressions have been used as effective methods for variable selection and estimation in high-dimensional models. In penalized regressions, it is common practice to standardize variables before fitting a penalized model and then fit a penalized model with standardized variables. Finally, the estimated coefficients from a penalized model are recovered to the scale on original variables. However, these procedures produce a slightly different solution compared to the corresponding original penalized problem. In this paper, we investigate issues on the standardization of variables in penalized regressions and formulate the definition of the standardized penalized estimator. In addition, we compare the original penalized estimator with the standardized penalized estimator through simulation studies and real data analysis.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.2
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• pp.809-818
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• 2019

The current trend in shipyard industry is to reduce the weight of ships to support the reduction of CO₂ emissions. In this study, the stiffened plate was optimized that is used for building most of the ship-structure. Further, this study proposed the hybrid Genetic Algorithm (GA) technique, which combines a genetic algorithm and subsequent optimization methods. The design variables included the number and type of stiffeners, stiffener spacing, and plate thickness. The number and type of stiffeners are discrete design variables that were optimized using the genetic algorithm. The stiffener spacing and plate thickness are continuous design variables that were determined by subsequent optimization. The plate deformation was classified into global and local displacement, resulting in accurate estimations of the maximum displacement. The optimization result showed that the proposed hybrid GA is effective for obtaining optimal solutions, for all the design variables.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.17 no.6
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• pp.587-595
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• 2005

The heat and flow characteristics in a parallel-flow heat exchanger were examined numerically to obtain its optimal design variables. A desirability function approach was introduced to optimize its performance with respect to the design parameters over the design domain. By varying the importance of heat transfer and pressure drop which are out put variables, the optimal values of the design parameters are examined. As a result, the us-age of the desirability function is very effective for the optimization of the design variables in a heat exchanger since the changes of optimal values are physically appropriate by varying the importance of each output variable.

• Journal of the Korean Home Economics Association
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• v.32 no.4
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• pp.57-71
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• 1994

The purpose of this research was to study the variables related to the stress and to provide the way of establishing better relationship between aged parents and adult offsprings and the way of effective care-giving. In order to achieve this purpose a survey was conducted by interview using questionaire. The collected data were analyzed by using frequency percentage mean standard deviation factor analysis Pearson's correlation analysis, ANOVA, and stepwise regression analysis. The results of this research were as follows: First The total points of daughter-in-laws' caregicing stress was 38.5 and daughter's caregiving stress was 27.3. Second There were meaningful differences according to living arrangement daughters-in-law age, old mothers' health status. And there were significant. interaction effects among variables of living arrangement and daughters-in-law' age, living arrangement and birth order birth order and old mothers' health status. living arrangement and daughters-in-law' age and old mothers' health status. Third There were meaningful differences according to living arrangement daughter's age. And there were significant interaction effects among variables of living arrangement and daughter's age, daughter's age and birth order, living arrangement and daughter's age and old mothers' health status, living arrangement and birth order and old mothers' health status. Fourth Among daughters-in-law' variables living arrangement age, old mothers' health status have influence on the care-giving stress. Among daughter's variables living arrangement, age, birth order have influence on the care-giving stress.